BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis

• URL: http://arxiv.org/abs/2203.11702v1
• Date: Tue, 22 Mar 2022 13:12:27 GMT
• Title: BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis
• Authors: Ahmed Murtadha, Shengfeng Pan, Bo Wen, Jianlin Su, Wenze Zhang, Yunfeng Liu
• Abstract summary: We propose to address aspect categorization and aspect-based sentiment subtasks in a unified framework. We first introduce a mechanism that collaborates the semantic and syntactic information to construct auxiliary-sentences for the implicit aspect. We then encourage BERT to learn the aspect-specific representation in response to the automatically constructed auxiliary-sentence.
• Score: 4.008465268899542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aspect-based sentiment analysis (ABSA) task aims to associate a piece of text with a set of aspects and meanwhile infer their respective sentimental polarities. Up to now, the state-of-the-art approaches are built upon fine-tuning of various pre-trained language models. They commonly aim to learn the aspect-specific representation in the corpus. Unfortunately, the aspect is often expressed implicitly through a set of representatives and thus renders implicit mapping process unattainable unless sufficient labeled examples. In this paper, we propose to jointly address aspect categorization and aspect-based sentiment subtasks in a unified framework. Specifically, we first introduce a simple but effective mechanism that collaborates the semantic and syntactic information to construct auxiliary-sentences for the implicit aspect. Then, we encourage BERT to learn the aspect-specific representation in response to the automatically constructed auxiliary-sentence instead of the aspect itself. Finally, we empirically evaluate the performance of the proposed solution by a comparative study on real benchmark datasets for both ABSA and Targeted-ABSA tasks. Our extensive experiments show that it consistently achieves state-of-the-art performance in terms of aspect categorization and aspect-based sentiment across all datasets and the improvement margins are considerable.

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